

#chapter 4


import tensorflow as tf
sess = tf.InteractiveSession()

#step 1: define network forward function

x=tf.placeholder(tf.float32, [None, 784])
W1 = tf.Variable(tf.truncated_normal([784, 200], stddev=0.1))
b1 = tf.Variable(tf.zeros([200]))
hidden1=tf.nn.relu(tf.matmul(x, W1)  + b1)

keep_prob = tf.placeholder(tf.float32)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)

W2 = tf.Variable(tf.zeros([200, 10]))
b2 = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(hidden1_drop, W2) + b2)


#end step 1


#step 2a define loss function

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))

#end step 2a


#step 2b define optimizer

train_step=tf.train.GradientDescentOptimizer(0.3).minimize(cross_entropy)

#end step 2b

#step 2c define evaluate function

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#end step 2c


tf.global_variables_initializer().run()

#step 3a load training set

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#end step 3a

#step 3b loop training


for i in range(5000):
	batch_xs, batch_ys = mnist.train.next_batch(100)
	
	if i%100==0:
		train_accuracy=accuracy.eval(feed_dict={x:batch_xs, y_:batch_ys,keep_prob: 1.0})
		print("step %d, training accuracy %g"%(i, train_accuracy))	
		
	train_step.run({x: batch_xs, y_: batch_ys, keep_prob: 0.75})
	
#end step 3b


#step 4 evaluate on test set

print(accuracy.eval({x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))

#end step 4



import numpy
numpy.savetxt("/home/u/b1.csv", b1.eval(), delimiter=",")
numpy.savetxt("/home/u/W1.csv", W1.eval(), delimiter=",")
numpy.savetxt("/home/u/b2.csv", b2.eval(), delimiter=",")
numpy.savetxt("/home/u/W2.csv", W2.eval(), delimiter=",")


